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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Economic Journal of Emerging Markets Jurnal Ilmiah Poli Rekayasa Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal NERS Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) POLYGLOT Emerging Science Journal Syntax Literate: Jurnal Ilmiah Indonesia JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) International Journal of New Media Technology Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Supply Chain Management Poltekita : Jurnal Ilmu Kesehatan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Informatika Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika Ekonomi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences Walisongo Journal of Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Action Research Literate (ARL) Jurnal Algoritma Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Jurnal Informatika Ekonomi Bisnis Jurnal Sistem Informasi International Journal of Education, Language, Literature, Arts, Culture, and Social Humanities The Indonesian Journal of Computer Science Malahayati International Journal of Nursing and Health Science JHSS (Journal of Humanities and Social Studies) Jurnal Komtika (Komputasi dan Informatika) Sosio e-kons
Achmad Nizar Hidayanto
Fakultas Ilmu Komputer, Universitas Indonesia
Aerospace Engineering Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Pendekatan Deep Learning untuk Deteksi Kanker Payudara Menggunakan Concatenated ResNet50 dan ResNet152 Nur Nafiiyah; M. Lazuardi Elsony; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3259

Abstract

Breast cancer is one of the leading causes of death in women, so accurate early detection is key to improving patient survival rates. Although mammography is the standard method for breast cancer screening, manual interpretation of mammogram images still depends on the expertise of radiologists and has the potential to lead to misdiagnosis. This study proposes a concatenate transfer learning-based deep learning approach by combining two Residual Network architectures, namely ResNet50 and ResNet152, to improve feature representation capabilities in mammogram image classification. The dataset used is a combination of MIAS and CBIS-DDSM with two classes, namely benign and malignant. The evaluation was carried out using original test data without augmentation to ensure the objectivity of the results. The experimental results show that the proposed model achieves an average accuracy of 97.09% and outperforms several individual transfer learning models. The main contribution of this study lies in demonstrating that combining deep features from similar but different depth CNN architectures can improve classification stability and accuracy. These findings provide a conceptual basis for the development of more reliable deep learning-based medical decision support systems for early breast cancer detection.
Organizational and Strategy Impact Evaluation of CRM Implementation at Statistics Indonesia Geri Yesa Ermawan; Achmad Nizar Hidayanto; Hari Prasetyo Tri Wicaksono
Eduvest - Journal of Universal Studies Vol. 6 No. 2 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i2.52671

Abstract

The increasing adoption of digital systems in public sector organizations has positioned Customer Relationship Management (CRM) as a strategic instrument for improving service quality and organizational performance. This study aims to evaluate the organizational and strategic impacts of CRM implementation at Statistics Indonesia through the SILASTIK system. An applied research design with a case study approach was employed to examine CRM implementation within its institutional context. The evaluation was conducted using a multi-perspective performance evaluation framework focusing on organizational capital, human capital, customer retention, customer expansion, and customer perceived value. Data were collected through a structured questionnaire distributed to internal CRM users, open-ended survey responses, and a semi-structured interview with a key system stakeholder. Quantitative data were analyzed using descriptive analysis, while qualitative data were examined through thematic analysis and KPI categorization. The findings indicate that CRM implementation has contributed positively to organizational alignment, service coordination, and internal efficiency, particularly in terms of management commitment, knowledge sharing, and productivity. However, limitations were identified in behavior-oriented adoption, structured training mechanisms, system integration, and the systematic measurement of customer-oriented performance dimensions. Overall, the study demonstrates that CRM implementation at Statistics Indonesia has progressed beyond a technical system and now functions as an organizational enabler, although its strategic potential has not yet been fully realized. These findings provide empirical insights into CRM evaluation in public sector statistical institutions and offer a foundation for strengthening CRM governance and performance management.
Transfer Learning-Based Convolutional Neural Network for Classifying Organic and Recyclable Waste Nur Nafiiyah; M. Ari Zulkarnaen; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

The problem of waste management continues to increase along with population growth and lifestyle changes, highlighting the need for a fast and accurate waste classification system to support recycling processes. This study implements a transfer learning approach using seven Convolutional Neural Network (CNN) architectures: MobileNet, MobileNetV2, Xception, EfficientNetB0, VGG16, VGG19, and ResNet50 to classify waste into two categories: organic and recyclable. Each model is modified by adding a Global Average Pooling layer followed by a fully connected layer with 256 neurons before the output layer. The models are trained twice using 30 epochs, a batch size of 2, the Adam optimizer, and a learning rate of 0.0001. Experimental results show that ResNet50 achieves the best performance, with an accuracy of 89.84%, precision of 96.34%, recall of 82.82%, and an F1-score of 89.07%, followed by MobileNet with an accuracy of 89.25%. In contrast, Xception demonstrates the lowest performance, with an accuracy of 83.81%. Analysis of training and validation curves indicates that ResNet50 and MobileNet exhibit better stability and lower overfitting tendencies compared to other models.
Factors Affecting Switching Intention from Cash on Delivery to E-Payment Services in C2C E-Commerce Transactions: COVID-19, Transaction, and Technology Perspectives Betty Purwandari; Syahrul Alam Suriazdin; Achmad Nizar Hidayanto; Suryana Setiawan; Kongkiti Phusavat; Mutia Maulida
Emerging Science Journal Vol. 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-010

Abstract

During the COVID-19 pandemic, the application of e-payment has rapidly increased. However, e-payment has not been able to achieve a trustworthy level in e-commerce transactions. Thus, cash payment methods with Cash On Delivery (COD) services still dominate C2C e-commerce payment transactions in Indonesia. This study aims to investigate factors that affect users' switching intentions from COD to e-payment services. The research model was adopted by using the Push-Pull-Mooring framework, integrating perceived COVID-19 risk, technology acceptance, and transaction effort. Empirical research was conducted using data from 546 COD and e-payment users in Indonesia, with Structural Equation Modelling (SEM) being used to validate the model and analyze the hypotheses. The results indicate that switching intention from COD to e-payment is significantly influenced by pull factors in e-payment, which are economic benefits, performance expectancy, effort expectancy, and critical mass. There are also two mooring factors that significantly influence the switching intention from COD to e-payment, which are trust and perceived security and privacy. This study makes a significant contribution to the literature in terms of validating a theoretical framework that emphasizes factors that influence user switching intentions from COD to e-payment in the context of the COVID-19 pandemic. This research can be a reference for Indonesian payment system regulators and e-payment service providers in formulating regulations and strategies to accelerate the spread of digital transactions in Indonesia. Doi: 10.28991/esj-2022-SPER-010 Full Text: PDF
Critical Success Factors For Hybrid Cloud Data Integration: A Case Study In The Cement Industry Agil Angga Saputra; Achmad Nizar Hidayanto; Rani Fersari Damanik; Nurul Nadiyatun Sholihah; Dandi Taufiqurrohman
JHSS (JOURNAL OF HUMANITIES AND SOCIAL STUDIES) Vol. 10 No. 02. (2026): JHSS (Journal of Humanities and Social Studies)
Publisher : UNIVERSITAS PAKUAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/jhss.v10i02..280

Abstract

Data integration in hybrid cloud environments is a major challenge in the era of digital transformation due to the need to manage data efficiently across both public and private cloud environments. This study aims to identify and rank the Critical Success Factors (CSFs) for data integration in hybrid cloud environments, using a case study of PT XYZ, which is currently undergoing a cloud migration process. The study integrates three frameworks: TOE (Technology–Organization–Environment), HOT-fit (Human–Organization–Technology), and the IS Triangle, to form a holistic evaluation model with five dimensions. Twenty CSF factors were identified through a literature review and validated through interviews with five experts. Subsequently, the Analytical Hierarchy Process (AHP) method was applied to a questionnaire distributed to ten expert respondents to rank these factors. Of the five dimensions, the Business dimension was found to be the most important (weight 0.33), followed by Environment (0.18), Organization (0.17), Technology (0.17), and Human (0.15). These findings provide practical guidance for organizations implementing hybrid cloud data integration, particularly within the context of Indonesian regulations.
CNN-LSTM with Multi-Acoustic Features for Automatic Tajweed Mad Rule Classification Nenny Anggraini; Yusuf Rahman; Achmad Nizar Hidayanto; Husni Teja Sukmana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1062

Abstract

The rules of mad recitation in the Qur’an are a crucial aspect of tajwīd, governing the lengthening of vowel sounds that affect both meaning and recitational accuracy. Despite its importance, there is currently no reliable automatic system capable of classifying mad rules based on voice input. This study proposes a deep learning-based approach using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to automatically classify mad rules from Qur’anic recitations. The research follows the CRISP-DM methodology, covering data understanding, preparation, modeling, and evaluation stages. Acoustic features were extracted from 3,816 annotated audio segments of Surah Al-Fātiḥah, combining Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Root Mean Square (RMS) to represent phonetic and prosodic attributes. The CNN layers captured spatial characteristics of the spectrum, while LSTM layers modeled temporal dependencies of the audio. Experimental results show that the combination of all four features achieved an accuracy of 97.21%, precision of 95.28%, recall of 95.22%, and F1-score of 95.25%. These findings indicate that multi-feature integration enhances model robustness and interpretability. The proposed CNN-LSTM framework demonstrates potential for practical deployment in voice-based tajwīd learning tools and contributes to the broader field of Qur’anic speech recognition by offering a systematic, ethically grounded, and data-driven approach to mad classification.
KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT BERBASIS TRANSFER LEARNING CONVOLUTIONAL NEURAL NETWORK Nur Nafiiyah; Mohamad Habib Havito Ihzami; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4539

Abstract

The ripeness level of oil palm fruit directly affects the quality and yield of palm oil, making accurate and objective classification methods essential. This study aims to evaluate and compare the performance of several transfer learning Convolutional Neural Network (CNN) architectures for oil palm ripeness classification. The dataset used is a secondary dataset obtained from previous research and consists of four ripeness classes: under-ripe, unripe, ripe, and over-ripe. Data augmentation was applied only to the training data to increase data variability, while the original, non-augmented data were used for testing to ensure an objective evaluation. Seven CNN architectures were evaluated, namely ConvNeXt-Tiny, DenseNet121, InceptionV3, MobileNetV2, NASNetLarge, ResNet50, and Xception, using the same training configuration. The results show that ConvNeXt-Tiny and ResNet50 achieved the best performance, with accuracy and F1-scores of 97.73%. These findings indicate that efficient CNN architectures can provide optimal performance for oil palm ripeness classification.  
User Perceptions Of The Security Of Personal Health Data On Chatgpt In Indonesia Kezia Amelia Putri; Nerissa Arviana; Camelia Fikrillah; Nenden C.S. Ningrum; Achmad Nizar Hidayanto
JHSS (JOURNAL OF HUMANITIES AND SOCIAL STUDIES) Vol. 10 No. 01 (2026): JHSS (Journal of Humanities and Social Studies) (SI)
Publisher : UNIVERSITAS PAKUAN

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Abstract

This study aims to explore the relationship between perceptions of data security and privacy, awareness of cyberattacks, and other factors that influence users’ trust in ChatGPT and their willingness to provide personal information. Data was collected via a questionnaire and analyzed using SmartPLS. This study examines eight main variables, namely: Privacy Concern (PC), Perceived Effectiveness of Government Regulation (PEGR), Trust in ChatGPT (TR), Moral Motive (MM), Perceived Intelligence (PI), Perceived Risk (PR), Perceived Benefit (PB), and Sharing Willingness (SW). This study tested nine hypotheses regarding the influence of these variables. Data were collected via a questionnaire using a 1–5 Likert scale and analyzed using SmartPLS to test the relationships among constructs using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The analysis results indicate that four of the proposed hypotheses were supported, with p-values < 0.05, suggesting that the decision to share personal data is significantly influenced by perceived benefits, which are in turn influenced by perceptions of ChatGPT’s intelligence and trust in the platform. Perceived Risk does not have a significant impact on Sharing Willingness; however, privacy concerns influence users’ perceptions of risk. The results of this study indicate that the benefits of using ChatGPT, significantly influences their willingness to share personal health data. This study suggests that the sample size should be increased to conduct a more in-depth analysis of information-sharing decisions.
Peramalan Penerimaan Pajak Daerah Menggunakan ARIMA, LSTM, dan XGBoost: Studi Kasus Kabupaten Bantul Brian Rizadhani Latuconsina; Achmad Nizar Hidayanto
Sosio e-Kons Vol. 18 No. 2 (2026): Sosio e-Kons
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/sosioekons.v18i2.4200

Abstract

Pendapatan Asli Daerah (PAD) Kabupaten Bantul yang bersumber dari pajak hotel, restoran, dan parkir telah dipungut secara elektronik melalui perangkat tapping box. Namun, data transaksi yang telah terkumpul selama bertahun-tahun belum dimanfaatkan secara optimal untuk mendukung peramalan penerimaan daerah. Hasil wawancara dengan Badan Pengelolaan Keuangan, Pendapatan, dan Aset Daerah (BPKPAD) Kabupaten Bantul menunjukkan bahwa proses estimasi penerimaan selama ini masih dilakukan berdasarkan intuisi dan analisis tren sederhana menggunakan aplikasi spreadsheet. Penelitian ini bertujuan untuk mengidentifikasi model peramalan yang paling akurat untuk penerimaan pajak hotel, restoran, dan parkir dengan membandingkan metode ARIMA dan SARIMA sebagai pendekatan statistik, Long Short-Term Memory (LSTM) sebagai pendekatan deep learning, serta XGBoost sebagai pendekatan machine learning berdasarkan kerangka kerja CRISP-DM. Data yang digunakan terdiri atas sekitar 4,8 juta transaksi tapping box periode 2020–2025 yang diolah menjadi data deret waktu dan dievaluasi menggunakan metode rolling-origin backtest dengan indikator Root Mean Square Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa tidak terdapat satu model yang secara konsisten memberikan akurasi terbaik pada seluruh sektor dan horizon peramalan. XGBoost menghasilkan akurasi tertinggi pada sektor restoran dengan nilai MAPE terendah sebesar 1,86%, sedangkan ARIMA(1,1,1) menjadi model terbaik pada sektor parkir dengan nilai MAPE berkisar antara 7% hingga 9%. Pada sektor hotel yang memiliki tingkat volatilitas tinggi, model Seasonal Naïve memberikan hasil terbaik dengan nilai MAPE antara 10% hingga 14%. Temuan ini menunjukkan bahwa pemilihan model peramalan harus disesuaikan dengan karakteristik masing-masing sektor serta menegaskan pentingnya kualitas data tapping box dalam mendukung perencanaan penerimaan daerah yang lebih akurat dan berbasis data.
Co-Authors . Herianto . Herianto . Herianto . Herianto Ade Irma Suryani Adhiawan Soegiharto Agil Angga Saputra Agri Fina Agung Terminanto Agus Harjoko Agus Harjoko Ahmadin, Yudhiansyah Aisha Arthamevia Ajie Tri Hutama Alfatianisa, Kirana Alfiany, Noverina Aniati Murni Arymurthy Anita Muliawati Antia Antia Ardiati Utami Sarjono Asymala, Asymala Permata Sari Atalya Yoseba S. Ayuning Budi, Nur Fitriah Azainil Azainil Barus, Okky Beny Maulana Achsan Bob Hardian Syahbuddin Brian Rizadhani Latuconsina Cahyaningtyas, Astri Camelia Fikrillah Canrakerta Canrakerta Canrakerta, Canrakerta Chris Solontio Dandi Taufiqurrohman Debi Setiawan Debi Setiawan, Debi Devi Fitrianah Dewi Puspa Dewi Puspasari Diah Kumalasari Dian Setia Hartana Dian Setia Hartana Dian Setia Hartana Diane Fitria Dwiza Riana Dyna Marissa Khairina Ejo Imandeka Ernawati Pasaribu Fahmi, Rizki Ali Fajar Budi Utomo Fakhri Mubarak, Muhammad Fatimah Azzahro Febiani, Dyah Ayu Fitri Kartiasih Fitri, Imelda Friendly Nur Shakti Geri Yesa Ermawan Geri Yesa Ermawan Gusmao, Mazarino Neil Araujo Pires Leite Handayani, Putu Wuri Handini Mekkawati Hanny Handiyani Hapsari, Ika Chandra Hari Prasetyo Tri Wicaksono Hari Prasetyo Tri Wicaksono Hartana, Dian Setia Henki Bayu Seta Hisyam Fahmi Hohashi, Naohiro Husni Teja Sukmana I Gusti Ngurah Adi Wicaksana Ihsan Lutfi Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Imairi Eitiveni Indra Budi Irfandi, Zikri Isal, Yugo Kartono J.W. Saputro J.W. Saputro J.W. Saputro Jwalita Galuh Garini Kamrozi Kemas Khaidar Ali Indrakusuma Kenedi Binowo Kezia Amelia Putri Kongkiti Phusavat Kongkiti Phusavat Kongkiti Phusavat Krishna Yudhakusuma P.M. Lasiyanto Putro, Pamuji M. Ari Zulkarnaen M. Aulia Hafidh M. Lazuardi Elsony Maemonah, Maemonah Mahdi, Askarul Mahmud, Mufti Mediati, Ati Surya Mediawati, Ati Surya Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Mohamad Habib Havito Ihzami Mohammed Al Kwarizmi Dwi Anggara Muh. Alviazra Virgananda Muhamad Ikbal Muhamad Raihan Fikriansyah Muhammad Daril Nofriansyah Muhammad Imam Santosa Muhammad Labib Jundillah Muhammad Rizky Anditama Muhammad Rizky Anditama Mutia Maulida Nafiiyah, Nur Naohiro Hohashi Nazar, Rizal Mochamad Nenden C.S. Ningrum Nenny Anggraini Nerissa Arviana Ni Wayan Trisnawaty Nilamsari Putri Utami Ninda Lutfiani Noratama Putri, Ramalia Nori Wilantika Noverina Alfiany Nugroho, Widijanto Satyo Nugroho, Widijanto Satyo Nur Fitriah Ayuning Budi Nur Fitriah Ayuning Budi Nur Nafiiyah Nurul Nadiyatun Sholihah Pamuji Lasiyanto Putro Panca O. Hadi Putra Pang Ning-Tan Pangesti, Dyah Pertiwi, Ratih Putri Prasetya, Roliand Prastiti, Rizdiani Tri Purwandari, Betty Putro, Prasetyo Adi Wibowo Qorib Munajat Qurotul Aini R. Yugo Kartono Isal Rahmad Mulyadi Ramadiani - Ramalia Noratama Putri Rani Fersari Damanik Rania Azzahra Rayhan Anandya Reihan Putra Oktavio Rifqi Firdaus Al Jauhari Rizha Febriyanti Rizki Tri Prasetio, Rizki Tri Robby Hermansyah Rosa Nur Rizky FT Rr Tutik Sri Hariyati Ryan Randy Suryono Samik-Ibrahim, Rahmat Mustafa Saputro, J.W. Sara Herlina Sartika Djamaluddin, Sartika Septian Bagus Wibisono Septian Bagus Wibisono Setiawati, Deni Setyowati , Setyowati Setyowati Setyowati Setyowati Setyowati Sherah Kurnia Sihotang, Jhon Rafles Simanjuntak, Almon Junior Sita Wardhani Solontio, Chris Suryana Setiawan Suryana Setiawan Syafiq Abdillah U. Syafira, Adinda Rizkita Syahrul Alam Suriazdin Syahrul Tuba Syanandi, Muhammad Destara Theresiawati Trimanadi, Raden Untung Rahardja Wachid Yoga Afrida Wahyu Catur Wibowo Widijanto Satyo Nugroho Wisnubroto, Agus Sigit Yova Ruldeviyani Yuda Irawan Yudhiansyah Ahmadin Yudhiansyah Ahmadin Yudhiansyah Ahmadin Yudhianto, Riswan Haryo Yudi Ramdhani Yunarso Anang Yusuf Rahman Zikri Irfandi